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Diagnosing The Causes of Poor Pharmaceutical Systems Performance

Marc J. RobertsProfessor of Political Economy and Health Policy

Harvard School of Public HealthPharmaceutical Workshop Nairobi 12 April, 2011

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Clarifying Concepts: Efficiency

• Technical efficiency: Producing outputs at minimum costs – drugs purchased and distributed at minimum cost (HOW outputs are produced)

• Allocative efficiency: Producing the right outputs to achieve our goals – buying the right drugs to provide cost effective care for the population (WHAT outputs are produced)

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Clarifying Concepts: Access

The ability of patients to use services That they want to use That experts believe they should use

Why Is Access Important?

• Influences utilization, health status and satisfaction

• Price barriers (formal and informal) undermine risk protection

• Political leaders often advocate for access as an end –we see it as a means

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Monitoring Access• Physical availability is not the same as effective

availability

• Low use can reflect demand factors as well as supply conditions

• To evaluating effective access requires a population survey of utilization and its determinants

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Finding the Causes of Poor Sector Pharmaceutical Performance :

Drawing A Diagnostic Tree

• Work “backward” from performance problems to causes and the causes of causes

• Ask “Why?” five times

• Seek to identify those causes that could be altered by policy changes

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What You Will Discover on Such a Journey

• Most performance problems have more than one cause

• Many causes have more than one effect

• Improving performance will often require more than one action

Example of a Diagnostic Tree

Poor Purchasing Lack of ACTs in Logistical Failures Public Clinics Theft Lack of Gov’t FundsHigh Levels of MalariaIn Rural Areas Lack of Retail High Prices for ACTs Competition in Private Sector No Retail Price Regulation

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Points About a “Diagnostic Tree”

• For each cause there can be more than one further cause

• Causes can impact more than one other cause

• The result may be messy—the arrows can cross

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Guidance on the Process

• Work backwards

• Do not jump to conclusions

• Be scientific

• Respect data

• Be specific

• Look for causes you can fix